賦值

> x <- 5  #賦值
> ls()    #檢視已經建立的變數
[1] "x"

> age <- c(1,3,5,2,11,9,3,9,12,3)
> weight <- c(4.4,5.3,7.2,5.2,8.5,7.3,6.0,10.4,10.2,6.1)
> mean(age)    #平均數
[1] 5.8
> sd(weight)    #標準差
[1] 2.077498
> cor(age,weight)    #相關係數
[1] 0.9075655
> plot(age,weight)    #畫圖
>
 
 demo()    #檢視r語言能畫的所有圖
> demo(graphics)    #檢視r語言能畫哪些圖

檢視幫助文件

> help.start()
starting httpd help server ... 做完了。
如果什麼都不發生的話，你應該自己開啟‘http://127.0.0.1:20557/doc/html/index.html’
> help("mean")
> ?mean
> ??car

工作空間

> getwd()
[1] "C:/Users/rdz/Documents"
> setwd("c:/Users/") 
> getwd
 
()
[1] "c:/Users"
> setwd( "C:/Users/rdz/Documents"
+ )
> getwd
function () 
.Internal(getwd())
<bytecode: 0x0000000004e9f580>
<environment: namespace:base>
> setwd("c:/Users/rdz/Documents")
> getwd()
[1] "c:/Users/rdz/Documents"

使用包

檢視r語言收集在內的所有包
https://cran.r-project.org/web/packages/

> library
 
()    #檢視安裝了哪些包
> help("base")
> help(package="base")
> help(package="car")
Error in find.package(pkgName, lib.loc, verbose = verbose) :
  there is no package called ‘car’
> install.packages("car")
> help(package="car")    #成功

#使用該包
> library(car)
> some

#更新所有包
update.packages()

結果重用

> head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
> lm(mpg~wt, data=mtcars)    #迴歸分析，lm(y~x1+x2...模型, 資料)

Call:
lm(formula = mpg ~ wt, data = mtcars)

Coefficients:
(Intercept)           wt  
     37.285       -5.344  
> result <- lm(mpg~wt, data=mtcars)    #儲存該回歸結果
> summary(result)    #查看回歸結果

Call:
lm(formula = mpg ~ wt, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5432 -2.3647 -0.1252  1.4096  6.8727 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
wt           -5.3445     0.5591  -9.559 1.29e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.046 on 30 degrees of freedom
Multiple R-squared:  0.7528,    Adjusted R-squared:  0.7446 
F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

> plot(result)    #畫圖
> predict(result, mynewdata)    #預測